Overview

Dataset statistics

Number of variables24
Number of observations812808
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory155.0 MiB
Average record size in memory200.0 B

Variable types

Categorical6
Numeric18

Alerts

hear_left has constant value ""Constant
hear_right has constant value ""Constant
urine_protein has constant value ""Constant
height is highly overall correlated with weight and 2 other fieldsHigh correlation
weight is highly overall correlated with height and 3 other fieldsHigh correlation
waistline is highly overall correlated with weightHigh correlation
sight_left is highly overall correlated with sight_rightHigh correlation
sight_right is highly overall correlated with sight_leftHigh correlation
SBP is highly overall correlated with DBPHigh correlation
DBP is highly overall correlated with SBPHigh correlation
tot_chole is highly overall correlated with LDL_choleHigh correlation
LDL_chole is highly overall correlated with tot_choleHigh correlation
hemoglobin is highly overall correlated with height and 2 other fieldsHigh correlation
serum_creatinine is highly overall correlated with sexHigh correlation
SGOT_AST is highly overall correlated with SGOT_ALTHigh correlation
SGOT_ALT is highly overall correlated with SGOT_AST and 1 other fieldsHigh correlation
gamma_GTP is highly overall correlated with SGOT_ALTHigh correlation
sex is highly overall correlated with height and 4 other fieldsHigh correlation
SMK_stat_type_cd is highly overall correlated with sexHigh correlation

Reproduction

Analysis started2023-09-16 11:23:21.688462
Analysis finished2023-09-16 11:25:56.456169
Duration2 minutes and 34.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

sex
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.2 MiB
1
410618 
0
402190 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters812808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 410618
50.5%
0 402190
49.5%

Length

2023-09-16T16:55:56.573493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T16:55:56.771119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 410618
50.5%
0 402190
49.5%

Most occurring characters

ValueCountFrequency (%)
1 410618
50.5%
0 402190
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 812808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 410618
50.5%
0 402190
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common 812808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 410618
50.5%
0 402190
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 812808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 410618
50.5%
0 402190
49.5%

age
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.693727
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:55:56.927708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q135
median45
Q355
95-th percentile70
Maximum85
Range65
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.912102
Coefficient of variation (CV)0.2979437
Kurtosis-0.56666157
Mean46.693727
Median Absolute Deviation (MAD)10
Skewness0.15804923
Sum37953035
Variance193.54658
MonotonicityNot monotonic
2023-09-16T16:55:57.096702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
40 110095
13.5%
50 106637
13.1%
45 99448
12.2%
55 90543
11.1%
60 84267
10.4%
35 71799
8.8%
30 67334
8.3%
25 57920
7.1%
65 40465
 
5.0%
70 36362
 
4.5%
Other values (4) 47938
5.9%
ValueCountFrequency (%)
20 20162
 
2.5%
25 57920
7.1%
30 67334
8.3%
35 71799
8.8%
40 110095
13.5%
45 99448
12.2%
50 106637
13.1%
55 90543
11.1%
60 84267
10.4%
65 40465
 
5.0%
ValueCountFrequency (%)
85 1747
 
0.2%
80 9176
 
1.1%
75 16853
 
2.1%
70 36362
 
4.5%
65 40465
 
5.0%
60 84267
10.4%
55 90543
11.1%
50 106637
13.1%
45 99448
12.2%
40 110095
13.5%

height
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.14691
Minimum130
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:55:57.277526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile150
Q1155
median160
Q3170
95-th percentile175
Maximum190
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.229772
Coefficient of variation (CV)0.056922282
Kurtosis-0.54991109
Mean162.14691
Median Absolute Deviation (MAD)5
Skewness0.02464736
Sum1.317943 × 108
Variance85.188691
MonotonicityNot monotonic
2023-09-16T16:55:57.448653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
160 152062
18.7%
165 142807
17.6%
155 141606
17.4%
170 132508
16.3%
150 90337
11.1%
175 79989
9.8%
145 30802
 
3.8%
180 29438
 
3.6%
140 6621
 
0.8%
185 5473
 
0.7%
Other values (3) 1165
 
0.1%
ValueCountFrequency (%)
130 53
 
< 0.1%
135 826
 
0.1%
140 6621
 
0.8%
145 30802
 
3.8%
150 90337
11.1%
155 141606
17.4%
160 152062
18.7%
165 142807
17.6%
170 132508
16.3%
175 79989
9.8%
ValueCountFrequency (%)
190 286
 
< 0.1%
185 5473
 
0.7%
180 29438
 
3.6%
175 79989
9.8%
170 132508
16.3%
165 142807
17.6%
160 152062
18.7%
155 141606
17.4%
150 90337
11.1%
145 30802
 
3.8%

weight
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.650017
Minimum25
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:55:57.649579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile45
Q155
median60
Q370
95-th percentile85
Maximum120
Range95
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.144865
Coefficient of variation (CV)0.19385255
Kurtosis0.23851359
Mean62.650017
Median Absolute Deviation (MAD)10
Skewness0.55355502
Sum50922435
Variance147.49775
MonotonicityNot monotonic
2023-09-16T16:55:57.837891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
55 128095
15.8%
60 126021
15.5%
65 115713
14.2%
50 108492
13.3%
70 98384
12.1%
75 71444
8.8%
45 54686
6.7%
80 44659
 
5.5%
85 24991
 
3.1%
40 13750
 
1.7%
Other values (10) 26573
 
3.3%
ValueCountFrequency (%)
25 4
 
< 0.1%
30 98
 
< 0.1%
35 1414
 
0.2%
40 13750
 
1.7%
45 54686
6.7%
50 108492
13.3%
55 128095
15.8%
60 126021
15.5%
65 115713
14.2%
70 98384
12.1%
ValueCountFrequency (%)
120 117
 
< 0.1%
115 319
 
< 0.1%
110 650
 
0.1%
105 1479
 
0.2%
100 3017
 
0.4%
95 6500
 
0.8%
90 12975
 
1.6%
85 24991
 
3.1%
80 44659
5.5%
75 71444
8.8%

waistline
Real number (ℝ)

HIGH CORRELATION 

Distinct692
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.395684
Minimum27
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:55:58.061553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile65
Q174
median80
Q387
95-th percentile96
Maximum130
Range103
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.3863507
Coefficient of variation (CV)0.11675192
Kurtosis-0.067912587
Mean80.395684
Median Absolute Deviation (MAD)6.5
Skewness0.1655121
Sum65346256
Variance88.103579
MonotonicityNot monotonic
2023-09-16T16:55:58.294052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 31533
 
3.9%
81 28625
 
3.5%
82 28009
 
3.4%
84 27410
 
3.4%
76 27015
 
3.3%
83 26334
 
3.2%
78 26299
 
3.2%
86 25759
 
3.2%
85 24534
 
3.0%
79 24501
 
3.0%
Other values (682) 542789
66.8%
ValueCountFrequency (%)
27 1
 
< 0.1%
30 1
 
< 0.1%
32 2
 
< 0.1%
35 1
 
< 0.1%
40 1
 
< 0.1%
43 1
 
< 0.1%
49 1
 
< 0.1%
50 5
< 0.1%
50.3 1
 
< 0.1%
50.5 2
 
< 0.1%
ValueCountFrequency (%)
130 3
 
< 0.1%
129 2
 
< 0.1%
127 5
< 0.1%
126.6 1
 
< 0.1%
126 2
 
< 0.1%
125 5
< 0.1%
124.5 1
 
< 0.1%
124.2 1
 
< 0.1%
124 9
< 0.1%
123.8 1
 
< 0.1%

sight_left
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96414934
Minimum0.1
Maximum2.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:55:58.511144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.7
median1
Q31.2
95-th percentile1.5
Maximum2.5
Range2.4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.3393459
Coefficient of variation (CV)0.35196405
Kurtosis0.068300039
Mean0.96414934
Median Absolute Deviation (MAD)0.2
Skewness0.04319651
Sum783668.3
Variance0.11515564
MonotonicityNot monotonic
2023-09-16T16:55:58.716384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 168161
20.7%
1.2 158996
19.6%
1.5 104596
12.9%
0.9 86787
10.7%
0.8 81221
10.0%
0.7 67158
 
8.3%
0.6 42124
 
5.2%
0.5 40534
 
5.0%
0.4 23575
 
2.9%
0.3 15548
 
1.9%
Other values (13) 24108
 
3.0%
ValueCountFrequency (%)
0.1 6888
 
0.8%
0.2 9274
 
1.1%
0.3 15548
 
1.9%
0.4 23575
 
2.9%
0.5 40534
 
5.0%
0.6 42124
 
5.2%
0.7 67158
 
8.3%
0.8 81221
10.0%
0.9 86787
10.7%
1 168161
20.7%
ValueCountFrequency (%)
2.5 6
 
< 0.1%
2.2 1
 
< 0.1%
2.1 2
 
< 0.1%
2 7302
 
0.9%
1.9 26
 
< 0.1%
1.8 22
 
< 0.1%
1.7 12
 
< 0.1%
1.6 293
 
< 0.1%
1.5 104596
12.9%
1.4 12
 
< 0.1%

sight_right
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96148119
Minimum0.1
Maximum2.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:55:58.916715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.7
median1
Q31.2
95-th percentile1.5
Maximum2.5
Range2.4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.33755081
Coefficient of variation (CV)0.35107376
Kurtosis0.051573498
Mean0.96148119
Median Absolute Deviation (MAD)0.2
Skewness0.011058999
Sum781499.6
Variance0.11394055
MonotonicityNot monotonic
2023-09-16T16:55:59.126337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 170969
21.0%
1.2 158286
19.5%
1.5 103351
12.7%
0.9 87220
10.7%
0.8 80508
9.9%
0.7 67399
 
8.3%
0.6 41858
 
5.1%
0.5 39694
 
4.9%
0.4 23932
 
2.9%
0.3 15419
 
1.9%
Other values (13) 24172
 
3.0%
ValueCountFrequency (%)
0.1 7242
 
0.9%
0.2 9928
 
1.2%
0.3 15419
 
1.9%
0.4 23932
 
2.9%
0.5 39694
 
4.9%
0.6 41858
 
5.1%
0.7 67399
 
8.3%
0.8 80508
9.9%
0.9 87220
10.7%
1 170969
21.0%
ValueCountFrequency (%)
2.5 8
 
< 0.1%
2.2 1
 
< 0.1%
2.1 9
 
< 0.1%
2 6279
 
0.8%
1.9 18
 
< 0.1%
1.8 27
 
< 0.1%
1.7 19
 
< 0.1%
1.6 305
 
< 0.1%
1.5 103351
12.7%
1.4 23
 
< 0.1%

hear_left
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.7 MiB
1.0
812808 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2438424
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 812808
100.0%

Length

2023-09-16T16:55:59.328643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T16:55:59.523606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 812808
100.0%

Most occurring characters

ValueCountFrequency (%)
1 812808
33.3%
. 812808
33.3%
0 812808
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1625616
66.7%
Other Punctuation 812808
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 812808
50.0%
0 812808
50.0%
Other Punctuation
ValueCountFrequency (%)
. 812808
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2438424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 812808
33.3%
. 812808
33.3%
0 812808
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2438424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 812808
33.3%
. 812808
33.3%
0 812808
33.3%

hear_right
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.7 MiB
1.0
812808 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2438424
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 812808
100.0%

Length

2023-09-16T16:55:59.689575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T16:55:59.884638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 812808
100.0%

Most occurring characters

ValueCountFrequency (%)
1 812808
33.3%
. 812808
33.3%
0 812808
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1625616
66.7%
Other Punctuation 812808
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 812808
50.0%
0 812808
50.0%
Other Punctuation
ValueCountFrequency (%)
. 812808
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2438424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 812808
33.3%
. 812808
33.3%
0 812808
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2438424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 812808
33.3%
. 812808
33.3%
0 812808
33.3%

SBP
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.47028
Minimum70
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:56:00.083191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile100
Q1111
median120
Q3130
95-th percentile145
Maximum197
Range127
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.117634
Coefficient of variation (CV)0.11622295
Kurtosis0.59370473
Mean121.47028
Median Absolute Deviation (MAD)10
Skewness0.40161441
Sum98732017
Variance199.30759
MonotonicityNot monotonic
2023-09-16T16:56:00.339386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 64949
 
8.0%
110 62378
 
7.7%
130 56082
 
6.9%
118 34011
 
4.2%
100 27340
 
3.4%
119 20357
 
2.5%
128 19188
 
2.4%
116 18933
 
2.3%
138 18846
 
2.3%
124 18307
 
2.3%
Other values (116) 472417
58.1%
ValueCountFrequency (%)
70 3
 
< 0.1%
73 4
 
< 0.1%
74 2
 
< 0.1%
75 7
 
< 0.1%
76 6
 
< 0.1%
77 6
 
< 0.1%
78 11
 
< 0.1%
79 5
 
< 0.1%
80 106
< 0.1%
81 82
< 0.1%
ValueCountFrequency (%)
197 8
 
< 0.1%
196 10
 
< 0.1%
195 15
 
< 0.1%
194 9
 
< 0.1%
193 10
 
< 0.1%
192 20
 
< 0.1%
191 12
 
< 0.1%
190 89
< 0.1%
189 32
 
< 0.1%
188 34
 
< 0.1%

DBP
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.538216
Minimum33
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:56:00.589004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile60
Q170
median76
Q381
95-th percentile90
Maximum124
Range91
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.6466389
Coefficient of variation (CV)0.12770541
Kurtosis0.41680189
Mean75.538216
Median Absolute Deviation (MAD)6
Skewness0.308858
Sum61398066
Variance93.057642
MonotonicityNot monotonic
2023-09-16T16:56:00.822757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 98221
 
12.1%
70 94542
 
11.6%
78 37022
 
4.6%
60 36180
 
4.5%
72 28502
 
3.5%
75 27023
 
3.3%
74 26611
 
3.3%
76 26479
 
3.3%
82 21850
 
2.7%
68 21271
 
2.6%
Other values (81) 395107
48.6%
ValueCountFrequency (%)
33 1
 
< 0.1%
34 1
 
< 0.1%
36 2
 
< 0.1%
37 2
 
< 0.1%
38 1
 
< 0.1%
39 3
 
< 0.1%
40 12
< 0.1%
41 5
< 0.1%
42 9
< 0.1%
43 11
< 0.1%
ValueCountFrequency (%)
124 30
 
< 0.1%
123 18
 
< 0.1%
122 22
 
< 0.1%
121 19
 
< 0.1%
120 216
< 0.1%
119 39
 
< 0.1%
118 59
 
< 0.1%
117 45
 
< 0.1%
116 69
 
< 0.1%
115 94
< 0.1%

BLDS
Real number (ℝ)

Distinct130
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.907917
Minimum30
Maximum164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:56:01.065223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile78
Q188
median95
Q3103
95-th percentile124
Maximum164
Range134
Interquartile range (IQR)15

Descriptive statistics

Standard deviation14.290882
Coefficient of variation (CV)0.14746867
Kurtosis2.9831646
Mean96.907917
Median Absolute Deviation (MAD)7
Skewness1.2824403
Sum78767530
Variance204.22931
MonotonicityNot monotonic
2023-09-16T16:56:01.313793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92 30624
 
3.8%
93 30470
 
3.7%
95 30405
 
3.7%
94 30396
 
3.7%
91 29904
 
3.7%
90 29478
 
3.6%
96 29091
 
3.6%
97 28229
 
3.5%
89 28158
 
3.5%
98 27163
 
3.3%
Other values (120) 518890
63.8%
ValueCountFrequency (%)
30 1
 
< 0.1%
32 1
 
< 0.1%
34 2
< 0.1%
36 1
 
< 0.1%
37 1
 
< 0.1%
38 2
< 0.1%
40 1
 
< 0.1%
42 4
< 0.1%
43 2
< 0.1%
44 3
< 0.1%
ValueCountFrequency (%)
164 348
< 0.1%
163 365
< 0.1%
162 404
< 0.1%
161 375
< 0.1%
160 426
0.1%
159 437
0.1%
158 491
0.1%
157 473
0.1%
156 524
0.1%
155 493
0.1%

tot_chole
Real number (ℝ)

HIGH CORRELATION 

Distinct333
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.8582
Minimum55
Maximum393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:56:01.564122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile138
Q1169
median193
Q3218
95-th percentile258
Maximum393
Range338
Interquartile range (IQR)49

Descriptive statistics

Standard deviation36.678616
Coefficient of variation (CV)0.18823235
Kurtosis0.37133922
Mean194.8582
Median Absolute Deviation (MAD)24
Skewness0.3682965
Sum1.583823 × 108
Variance1345.3208
MonotonicityNot monotonic
2023-09-16T16:56:01.791677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199 9328
 
1.1%
189 9215
 
1.1%
184 9165
 
1.1%
186 9131
 
1.1%
190 9116
 
1.1%
188 9099
 
1.1%
196 9067
 
1.1%
197 9064
 
1.1%
187 9061
 
1.1%
192 9052
 
1.1%
Other values (323) 721510
88.8%
ValueCountFrequency (%)
55 1
 
< 0.1%
57 1
 
< 0.1%
58 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
63 1
 
< 0.1%
64 5
< 0.1%
65 2
 
< 0.1%
67 1
 
< 0.1%
68 3
< 0.1%
ValueCountFrequency (%)
393 2
 
< 0.1%
392 1
 
< 0.1%
391 1
 
< 0.1%
388 1
 
< 0.1%
387 2
 
< 0.1%
386 3
< 0.1%
385 2
 
< 0.1%
384 4
< 0.1%
383 5
< 0.1%
382 1
 
< 0.1%

HDL_chole
Real number (ℝ)

Distinct136
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.562079
Minimum1
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:56:02.033074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37
Q147
median56
Q366
95-th percentile84
Maximum136
Range135
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.709993
Coefficient of variation (CV)0.25555007
Kurtosis0.77459366
Mean57.562079
Median Absolute Deviation (MAD)10
Skewness0.72727455
Sum46786918
Variance216.3839
MonotonicityNot monotonic
2023-09-16T16:56:02.291986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 24186
 
3.0%
53 23398
 
2.9%
52 23352
 
2.9%
54 23245
 
2.9%
51 23059
 
2.8%
49 22677
 
2.8%
55 22486
 
2.8%
56 22345
 
2.7%
48 22203
 
2.7%
57 21639
 
2.7%
Other values (126) 584218
71.9%
ValueCountFrequency (%)
1 2
< 0.1%
2 4
< 0.1%
3 1
 
< 0.1%
4 3
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 3
< 0.1%
9 4
< 0.1%
10 3
< 0.1%
ValueCountFrequency (%)
136 14
 
< 0.1%
135 23
< 0.1%
134 25
< 0.1%
133 19
< 0.1%
132 26
< 0.1%
131 24
< 0.1%
130 38
< 0.1%
129 23
< 0.1%
128 38
< 0.1%
127 35
< 0.1%

LDL_chole
Real number (ℝ)

HIGH CORRELATION 

Distinct295
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.36211
Minimum1
Maximum295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:56:02.557106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile62
Q190
median111
Q3135
95-th percentile171
Maximum295
Range294
Interquartile range (IQR)45

Descriptive statistics

Standard deviation33.575489
Coefficient of variation (CV)0.2961791
Kurtosis0.38266588
Mean113.36211
Median Absolute Deviation (MAD)22
Skewness0.38933664
Sum92141632
Variance1127.3135
MonotonicityNot monotonic
2023-09-16T16:56:02.787445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109 9954
 
1.2%
104 9914
 
1.2%
107 9878
 
1.2%
110 9853
 
1.2%
102 9846
 
1.2%
112 9804
 
1.2%
108 9802
 
1.2%
115 9783
 
1.2%
106 9782
 
1.2%
111 9723
 
1.2%
Other values (285) 714469
87.9%
ValueCountFrequency (%)
1 24
< 0.1%
2 3
 
< 0.1%
3 6
 
< 0.1%
4 3
 
< 0.1%
5 7
 
< 0.1%
6 8
 
< 0.1%
7 10
< 0.1%
8 15
< 0.1%
9 11
< 0.1%
10 16
< 0.1%
ValueCountFrequency (%)
295 3
< 0.1%
294 1
 
< 0.1%
293 3
< 0.1%
292 6
< 0.1%
291 2
 
< 0.1%
290 3
< 0.1%
289 2
 
< 0.1%
288 6
< 0.1%
287 6
< 0.1%
286 3
< 0.1%

triglyceride
Real number (ℝ)

Distinct460
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.1464
Minimum1
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:56:03.035957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q171
median102
Q3149
95-th percentile262
Maximum460
Range459
Interquartile range (IQR)78

Descriptive statistics

Standard deviation69.596899
Coefficient of variation (CV)0.57926746
Kurtosis3.0033508
Mean120.1464
Median Absolute Deviation (MAD)36
Skewness1.5753882
Sum97655955
Variance4843.7284
MonotonicityNot monotonic
2023-09-16T16:56:03.315425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 7249
 
0.9%
69 7153
 
0.9%
70 7151
 
0.9%
66 7143
 
0.9%
78 7126
 
0.9%
68 7089
 
0.9%
75 7084
 
0.9%
77 7069
 
0.9%
76 7068
 
0.9%
67 7063
 
0.9%
Other values (450) 741613
91.2%
ValueCountFrequency (%)
1 4
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 9
< 0.1%
8 4
< 0.1%
9 8
< 0.1%
10 6
< 0.1%
ValueCountFrequency (%)
460 49
< 0.1%
459 46
< 0.1%
458 55
< 0.1%
457 45
< 0.1%
456 51
< 0.1%
455 58
< 0.1%
454 70
< 0.1%
453 42
< 0.1%
452 60
< 0.1%
451 53
< 0.1%

hemoglobin
Real number (ℝ)

HIGH CORRELATION 

Distinct154
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.163037
Minimum5.5
Maximum22.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:56:03.561190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.5
5-th percentile11.7
Q113.1
median14.2
Q315.3
95-th percentile16.5
Maximum22.7
Range17.2
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.5547621
Coefficient of variation (CV)0.10977604
Kurtosis0.65041168
Mean14.163037
Median Absolute Deviation (MAD)1.1
Skewness-0.37296353
Sum11511830
Variance2.4172853
MonotonicityNot monotonic
2023-09-16T16:56:03.818582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.5 19809
 
2.4%
13.6 19721
 
2.4%
13.4 19668
 
2.4%
13.3 19563
 
2.4%
13.8 19391
 
2.4%
14 19329
 
2.4%
13.7 19107
 
2.4%
13.9 19067
 
2.3%
13.2 19063
 
2.3%
13.1 18768
 
2.3%
Other values (144) 619322
76.2%
ValueCountFrequency (%)
5.5 9
 
< 0.1%
5.6 7
 
< 0.1%
5.7 8
 
< 0.1%
5.8 14
< 0.1%
5.9 10
 
< 0.1%
6 19
< 0.1%
6.1 16
< 0.1%
6.2 20
< 0.1%
6.3 26
< 0.1%
6.4 23
< 0.1%
ValueCountFrequency (%)
22.7 1
 
< 0.1%
21.7 2
 
< 0.1%
21.6 2
 
< 0.1%
20.9 1
 
< 0.1%
20.5 1
 
< 0.1%
20.3 1
 
< 0.1%
20.2 2
 
< 0.1%
20.1 3
 
< 0.1%
20 2
 
< 0.1%
19.9 10
< 0.1%

urine_protein
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.7 MiB
1.0
812808 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2438424
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 812808
100.0%

Length

2023-09-16T16:56:04.347441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T16:56:04.542871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 812808
100.0%

Most occurring characters

ValueCountFrequency (%)
1 812808
33.3%
. 812808
33.3%
0 812808
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1625616
66.7%
Other Punctuation 812808
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 812808
50.0%
0 812808
50.0%
Other Punctuation
ValueCountFrequency (%)
. 812808
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2438424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 812808
33.3%
. 812808
33.3%
0 812808
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2438424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 812808
33.3%
. 812808
33.3%
0 812808
33.3%

serum_creatinine
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84348776
Minimum0.1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:56:04.710259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q10.7
median0.8
Q31
95-th percentile1.2
Maximum2
Range1.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.19596075
Coefficient of variation (CV)0.23232198
Kurtosis0.51428834
Mean0.84348776
Median Absolute Deviation (MAD)0.1
Skewness0.39821114
Sum685593.6
Variance0.038400616
MonotonicityNot monotonic
2023-09-16T16:56:04.911178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.8 161910
19.9%
0.9 146637
18.0%
0.7 139270
17.1%
1 112728
13.9%
0.6 94323
11.6%
1.1 68637
8.4%
0.5 33614
 
4.1%
1.2 31549
 
3.9%
1.3 11248
 
1.4%
0.4 5125
 
0.6%
Other values (10) 7767
 
1.0%
ValueCountFrequency (%)
0.1 339
 
< 0.1%
0.2 79
 
< 0.1%
0.3 474
 
0.1%
0.4 5125
 
0.6%
0.5 33614
 
4.1%
0.6 94323
11.6%
0.7 139270
17.1%
0.8 161910
19.9%
0.9 146637
18.0%
1 112728
13.9%
ValueCountFrequency (%)
2 104
 
< 0.1%
1.9 122
 
< 0.1%
1.8 282
 
< 0.1%
1.7 361
 
< 0.1%
1.6 668
 
0.1%
1.5 1476
 
0.2%
1.4 3862
 
0.5%
1.3 11248
 
1.4%
1.2 31549
3.9%
1.1 68637
8.4%

SGOT_AST
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.585759
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:56:05.125579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q119
median22
Q327
95-th percentile38
Maximum59
Range58
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.3968447
Coefficient of variation (CV)0.31361486
Kurtosis2.5117214
Mean23.585759
Median Absolute Deviation (MAD)4
Skewness1.3079106
Sum19170694
Variance54.713311
MonotonicityNot monotonic
2023-09-16T16:56:05.336896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 57073
 
7.0%
21 55772
 
6.9%
19 55577
 
6.8%
22 53286
 
6.6%
18 52011
 
6.4%
23 49638
 
6.1%
24 45180
 
5.6%
17 44731
 
5.5%
25 40413
 
5.0%
16 36529
 
4.5%
Other values (49) 322598
39.7%
ValueCountFrequency (%)
1 10
 
< 0.1%
2 16
 
< 0.1%
3 12
 
< 0.1%
4 20
 
< 0.1%
5 29
 
< 0.1%
6 56
 
< 0.1%
7 109
 
< 0.1%
8 248
 
< 0.1%
9 492
 
0.1%
10 1472
0.2%
ValueCountFrequency (%)
59 457
 
0.1%
58 500
0.1%
57 594
0.1%
56 577
0.1%
55 735
0.1%
54 713
0.1%
53 851
0.1%
52 937
0.1%
51 1051
0.1%
50 1203
0.1%

SGOT_ALT
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.41217
Minimum1
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:56:05.563477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q114
median19
Q327
95-th percentile47
Maximum78
Range77
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.935693
Coefficient of variation (CV)0.53255411
Kurtosis3.0518213
Mean22.41217
Median Absolute Deviation (MAD)6
Skewness1.5989108
Sum18216791
Variance142.46077
MonotonicityNot monotonic
2023-09-16T16:56:05.795113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 42207
 
5.2%
14 41583
 
5.1%
16 41444
 
5.1%
17 40263
 
5.0%
13 38944
 
4.8%
18 38642
 
4.8%
12 36474
 
4.5%
19 36230
 
4.5%
20 33510
 
4.1%
11 31761
 
3.9%
Other values (68) 431750
53.1%
ValueCountFrequency (%)
1 26
 
< 0.1%
2 53
 
< 0.1%
3 164
 
< 0.1%
4 503
 
0.1%
5 1444
 
0.2%
6 2855
 
0.4%
7 5874
 
0.7%
8 10977
 
1.4%
9 17029
2.1%
10 28437
3.5%
ValueCountFrequency (%)
78 473
0.1%
77 442
0.1%
76 517
0.1%
75 466
0.1%
74 555
0.1%
73 570
0.1%
72 645
0.1%
71 616
0.1%
70 652
0.1%
69 689
0.1%

gamma_GTP
Real number (ℝ)

HIGH CORRELATION 

Distinct119
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.436825
Minimum1
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 MiB
2023-09-16T16:56:06.094020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q115
median22
Q335
95-th percentile72
Maximum119
Range118
Interquartile range (IQR)20

Descriptive statistics

Standard deviation20.001477
Coefficient of variation (CV)0.70336532
Kurtosis3.7681075
Mean28.436825
Median Absolute Deviation (MAD)8
Skewness1.8862622
Sum23113679
Variance400.05906
MonotonicityNot monotonic
2023-09-16T16:56:06.350759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 36990
 
4.6%
15 36607
 
4.5%
13 35780
 
4.4%
16 35349
 
4.3%
17 33536
 
4.1%
12 33106
 
4.1%
18 31608
 
3.9%
19 29347
 
3.6%
11 28485
 
3.5%
20 27533
 
3.4%
Other values (109) 484467
59.6%
ValueCountFrequency (%)
1 14
 
< 0.1%
2 25
 
< 0.1%
3 149
 
< 0.1%
4 194
 
< 0.1%
5 545
 
0.1%
6 1417
 
0.2%
7 3153
 
0.4%
8 7440
 
0.9%
9 12801
1.6%
10 23083
2.8%
ValueCountFrequency (%)
119 428
0.1%
118 423
0.1%
117 476
0.1%
116 443
0.1%
115 469
0.1%
114 449
0.1%
113 534
0.1%
112 490
0.1%
111 544
0.1%
110 546
0.1%

SMK_stat_type_cd
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.2 MiB
1
511760 
3
164633 
2
136415 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters812808
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 511760
63.0%
3 164633
 
20.3%
2 136415
 
16.8%

Length

2023-09-16T16:56:06.582057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T16:56:06.813645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 511760
63.0%
3 164633
 
20.3%
2 136415
 
16.8%

Most occurring characters

ValueCountFrequency (%)
1 511760
63.0%
3 164633
 
20.3%
2 136415
 
16.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 812808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 511760
63.0%
3 164633
 
20.3%
2 136415
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
Common 812808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 511760
63.0%
3 164633
 
20.3%
2 136415
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 812808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 511760
63.0%
3 164633
 
20.3%
2 136415
 
16.8%

DRK_YN
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.2 MiB
0
412610 
1
400198 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters812808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 412610
50.8%
1 400198
49.2%

Length

2023-09-16T16:56:07.034626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T16:56:07.303644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 412610
50.8%
1 400198
49.2%

Most occurring characters

ValueCountFrequency (%)
0 412610
50.8%
1 400198
49.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 812808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 412610
50.8%
1 400198
49.2%

Most occurring scripts

ValueCountFrequency (%)
Common 812808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 412610
50.8%
1 400198
49.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 812808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 412610
50.8%
1 400198
49.2%

Interactions

2023-09-16T16:55:45.648277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:12.497509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:18.043140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:23.481194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:28.956542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:34.565511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:39.821178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:45.158917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:50.601328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:55.986486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:01.173548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:06.497635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:12.122644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:17.671841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:22.950625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:28.569847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:34.455562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:39.903292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:45.959215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:12.797798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:18.318115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:23.787155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:29.277855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:34.868645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:40.122831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:45.453879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:50.902262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:56.272078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:01.473280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:06.780591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:12.411511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:18.008960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:23.229933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:28.893328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:34.752453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:40.207560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:46.272795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:13.094997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:18.606621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:24.089561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:29.612307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:35.180781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:40.422857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:45.758884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:51.200943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:56.544589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:01.758791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:07.100218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:12.714335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:18.306482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:23.522798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:29.212841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:35.054387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:40.535048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:46.566380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:13.369295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:18.872046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:24.365152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:29.943736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:35.456254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:40.726650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:46.035347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:51.490823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:56.822376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:02.033877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:07.391685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:12.989650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:18.585377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:23.815725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:29.514761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:35.335798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:40.829244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:46.873444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:13.653398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:19.159832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:24.641263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:30.228899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:35.720402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:41.015526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:46.319975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:51.772767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:57.086732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:02.306267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:07.687294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:13.254942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:18.851279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:24.092850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:29.819114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:35.622388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:41.139706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:47.178856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:13.940836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:19.501997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:24.920994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:30.536494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:36.005873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:41.290025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:46.610942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:52.069703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:57.387763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:02.600367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:08.012015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:13.536305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:19.152172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:24.419915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:30.145595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:35.914085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:41.460178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:47.490569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:14.241797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:19.823828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:25.207401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:30.830201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:36.268842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:41.566658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:46.895408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:52.368662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:57.672830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:02.904149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:08.319263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:13.820066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:19.424480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:24.723854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:30.461449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:36.200197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:41.775767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:47.802417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:14.542290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:20.137777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:25.506331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:31.141162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:36.547702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:41.867702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:47.173194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:52.661665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:57.969497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:03.209920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:08.638427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:14.093434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:19.749871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:25.042136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:30.791000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:36.498790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:42.094057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:48.104384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:14.825860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:20.448127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:25.777815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:31.448765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:36.837883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:42.154313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:47.447262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:52.944975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:58.227655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:03.497736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:08.929122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:14.365440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:20.018524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:25.350209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:31.114090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:36.784036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:42.395024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:48.419990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:15.122401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:20.764755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:26.083688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:31.782825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:37.139388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:42.455671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:47.722797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:53.247613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:58.500987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:03.793777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:09.244524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:14.664910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:20.290882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:25.666650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:31.665143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:37.076536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:42.714664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:48.750770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:15.431122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:21.095033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:26.380048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:32.101141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:37.446144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:42.760438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:48.005116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:53.567213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:58.782420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:04.090412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:09.552233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:15.009678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:20.565192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:25.986798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:31.994359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:37.383214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:43.042529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:49.054703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:15.741978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:21.413412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:26.665265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:32.409370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:37.761117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:43.070508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:48.294796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:53.879144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:59.069265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:04.372092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:09.846364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:15.328027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:20.866102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:26.313261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:32.307094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:37.699052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:43.381192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:49.360808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:16.023515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:21.726402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:26.963253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:32.733904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:38.041381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:43.371146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:48.580437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:54.185085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:59.382332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:04.662599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:10.127535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:15.633412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:21.151178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:26.632052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:32.621436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:38.005725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:43.709817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:49.642315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:16.315665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:22.010811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:27.254136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:33.065211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:38.300464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:43.628028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:48.858502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:54.473291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:59.652284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:04.948433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:10.413769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:15.956298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:21.423031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:26.933623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:32.916678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:38.302037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:44.015067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:49.976541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:16.705387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:22.289800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:27.543197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:33.381711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:38.594934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:43.927749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:49.169836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:54.787558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:59.961002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:05.270444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:10.961408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:16.302202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:21.708256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:27.263716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:33.211286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:38.627931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:44.345452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:50.292300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:17.149572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:22.586231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:27.842725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:33.665834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:38.909456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:44.242623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:49.473007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:55.103581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:00.260869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:05.583151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:11.269380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:16.629516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:22.017199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:27.596844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:33.515777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:38.946977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:44.681619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:50.590434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:17.469897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:22.874633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:28.378906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:33.958428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:39.197451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:44.551122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:49.753405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:55.407768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:00.557721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:05.900243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:11.559008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:17.009416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:22.336189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:27.915011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:33.838886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:39.263221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:44.987212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:50.904286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:17.767785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:23.188797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:28.670326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:34.275605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:39.520143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:44.873389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:50.294294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:54:55.711645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:00.861461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:06.206230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:11.841718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:17.361879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:22.648725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:28.244999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:34.148418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:39.585234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:55:45.326919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-16T16:56:07.503297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ageheightweightwaistlinesight_leftsight_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPsexSMK_stat_type_cdDRK_YN
age1.000-0.375-0.1470.181-0.373-0.3670.2640.1410.2480.073-0.1200.0840.155-0.162-0.0120.2440.1050.0760.1280.1360.285
height-0.3751.0000.6770.3410.2690.2710.0570.1060.039-0.044-0.193-0.0160.1290.5880.4810.0570.2300.3160.7510.3620.363
weight-0.1470.6771.0000.7810.1640.1650.2680.2700.1910.046-0.3500.0810.3340.5410.4330.1810.4260.4540.6080.2900.258
waistline0.1810.3410.7811.000-0.012-0.0090.3480.2990.2640.076-0.3820.1040.4020.3920.3160.2490.4440.4610.4370.2100.114
sight_left-0.3730.2690.164-0.0121.0000.726-0.087-0.019-0.0740.0030.0050.003-0.0150.1760.098-0.0510.0310.0440.1740.0850.156
sight_right-0.3670.2710.165-0.0090.7261.000-0.083-0.016-0.0720.0030.0010.005-0.0120.1780.102-0.0490.0330.0460.1790.0860.154
SBP0.2640.0570.2680.348-0.087-0.0831.0000.7280.2290.073-0.1400.0540.2400.1870.1310.1900.2260.2670.2070.0950.052
DBP0.1410.1060.2700.299-0.019-0.0160.7281.0000.1840.104-0.1180.0800.2320.2380.1380.1650.2190.2720.1980.0970.080
BLDS0.2480.0390.1910.264-0.074-0.0720.2290.1841.0000.055-0.1340.0330.2350.1180.1210.1220.2030.2450.1330.0820.037
tot_chole0.073-0.0440.0460.0760.0030.0030.0730.1040.0551.0000.1670.8990.2660.0890.0260.1020.1180.1480.0490.0220.046
HDL_chole-0.120-0.193-0.350-0.3820.0050.001-0.140-0.118-0.1340.1671.000-0.061-0.465-0.247-0.227-0.093-0.251-0.2240.2990.1420.053
LDL_chole0.084-0.0160.0810.1040.0030.0050.0540.0800.0330.899-0.0611.0000.1320.1010.0570.0740.1080.0970.0410.0230.053
triglyceride0.1550.1290.3340.402-0.015-0.0120.2400.2320.2350.266-0.4650.1321.0000.2750.1800.1840.3350.4170.2340.1560.075
hemoglobin-0.1620.5880.5410.3920.1760.1780.1870.2380.1180.089-0.2470.1010.2751.0000.4860.2170.4040.4610.7170.3480.292
serum_creatinine-0.0120.4810.4330.3160.0980.1020.1310.1380.1210.026-0.2270.0570.1800.4861.0000.1780.2530.3320.6140.2710.193
SGOT_AST0.2440.0570.1810.249-0.051-0.0490.1900.1650.1220.102-0.0930.0740.1840.2170.1781.0000.6990.4010.1990.0910.033
SGOT_ALT0.1050.2300.4260.4440.0310.0330.2260.2190.2030.118-0.2510.1080.3350.4040.2530.6991.0000.5810.3510.1640.071
gamma_GTP0.0760.3160.4540.4610.0440.0460.2670.2720.2450.148-0.2240.0970.4170.4610.3320.4010.5811.0000.4530.2680.277
sex0.1280.7510.6080.4370.1740.1790.2070.1980.1330.0490.2990.0410.2340.7170.6140.1990.3510.4531.0000.6410.354
SMK_stat_type_cd0.1360.3620.2900.2100.0850.0860.0950.0970.0820.0220.1420.0230.1560.3480.2710.0910.1640.2680.6411.0000.349
DRK_YN0.2850.3630.2580.1140.1560.1540.0520.0800.0370.0460.0530.0530.0750.2920.1930.0330.0710.2770.3540.3491.000

Missing values

2023-09-16T16:55:51.479415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-16T16:55:53.038583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

sexageheightweightwaistlinesight_leftsight_righthear_lefthear_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPSMK_stat_type_cdDRK_YN
013517075.090.01.01.01.01.0120.080.099.0193.048.0126.092.017.11.01.021.035.040.011
113018080.089.00.91.21.01.0130.082.0106.0228.055.0148.0121.015.81.00.920.036.027.030
214016575.091.01.21.51.01.0120.070.098.0136.041.074.0104.015.81.00.947.032.068.010
315017580.091.01.51.21.01.0145.087.095.0201.076.0104.0106.017.61.01.129.034.018.010
415016560.080.01.01.21.01.0138.082.0101.0199.061.0117.0104.013.81.00.819.012.025.010
604515055.069.00.50.41.01.0101.058.089.0196.066.0115.075.012.31.00.819.012.012.010
713517565.084.21.21.01.01.0132.080.094.0185.058.0107.0101.014.41.00.818.018.035.031
815517075.084.01.20.91.01.0145.085.0104.0217.056.0141.0100.015.11.00.832.023.026.011
914017575.082.01.51.51.01.0132.0105.0100.0195.060.0118.083.013.91.00.921.038.016.021
1014515555.079.21.01.01.01.0118.070.090.0183.042.0130.055.012.91.00.819.014.019.010
sexageheightweightwaistlinesight_leftsight_righthear_lefthear_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPSMK_stat_type_cdDRK_YN
99133618017060.074.01.00.91.01.0139.083.0109.0171.075.084.057.012.01.01.218.011.015.021
99133703516570.081.01.01.01.01.0113.069.081.0173.063.092.088.013.31.00.720.017.012.010
99133812017565.074.51.01.51.01.0105.070.087.0211.072.0120.092.015.41.00.825.026.050.021
99133917016560.078.00.90.81.01.0137.078.093.0167.057.089.0105.016.11.01.023.013.032.011
99134005015050.072.61.01.01.01.0116.074.0108.0178.048.0105.0125.015.21.00.828.026.029.010
99134114517580.092.11.51.51.01.0114.080.088.0198.046.0125.0132.015.01.01.026.036.027.010
99134213517075.086.01.01.51.01.0119.083.083.0133.040.084.045.015.81.01.114.017.015.010
99134304015550.068.01.00.71.01.0110.070.090.0205.096.077.0157.014.31.00.830.027.017.031
99134412517560.072.01.51.01.01.0119.074.069.0122.038.073.053.014.51.00.821.014.017.010
99134515016070.090.51.01.51.01.0133.079.099.0225.039.0153.0163.015.81.00.924.043.036.031